Fake news detection focuses on identifying and preventing the spread of misleading or false information. It is crucial for maintaining the integrity of public discourse and protecting individuals from the harmful effects of misinformation. By ensuring the correctness and reliability of the information, the fake news detection hinders the loss of trust in the media, institutions, and public communication channels. The fake news detection system suggested is in the process of data acquisition where news stories are either manually or automatically retrieved from the net via web crawlers. The collected data later filters the information so it will use only credible sources. Phase two consists of the pre-processing phase using BERT, wherein the data will be tokenized and mapped into contextual embeddings that reflect the semantic meaning of words. Phase three is about engineering features using methods like TF-IDF and Word2vec to 
fine-tune the embeddings and label the important textual features. The final Phase of Classification occurs using the engineered features such that BERT-generated outputs are fine-tuned and passed through softmax functions to ascertain whether the news is fake or real. This holistic and all-encompassing approach integrates advanced natural language processing with feature engineering for an effective system concerning detection of fake news accurately. The model achieved remarkable results over various phases. Training accuracy went from 75% up to those above 95% whereas test accuracy tips above 90%, soaring from below 70%. The model's performance was validated with a balanced confusion matrix and a high ROC AUC of 0.94. Throughout different phases, accuracy, precision, recall, and F1-score increased, reaching 97.0%, 96.7%, 96.8%, and 96.9%, respectively, in the final classification phase, demonstrating robust and reliable detection capabilities.